In recent years, with rapid development of social informatization, data has shown an explosive growth in the fields of scientific research, industrial production, business, and Internet. Currently, data in many applications has developed rapidly from a terabyte (TB) level to a petabyte (PB) level or even a higher order of magnitude. Therefore, computing frameworks for big data processing become a hot topic. Representative computing frameworks include Hadoop and Spark. Frameworks such as Hadoop and Spark are widely applied in the field of computer technologies, but each of the computing frameworks has disadvantages. For example, a MapReduce model provided in Hadoop is easily applied, but a computing model has a limitation, the expressiveness is limited, and an algorithm is difficult to be mapped to the MapReduce model when complex problems such as iterative computation and diagram analysis are resolved. Moreover, workload for development is heavy, and the operating efficiency is low. Iterative operation performance of Spark is good, but a requirement on memory is high.
Therefore, a development trend of big data processing is to process big data using a data processing platform integrating multiple types of computing frameworks. That is, multiple types of computing frameworks are contained in a computer cluster using a resource management system, and typical resource management systems are, for example, Mesos and YARN.
However, multiple types of computing frameworks contained in the resource management system share one cluster resource, and programming languages of the computing frameworks are different. Therefore, when receiving a to-be-processed data task, a user usually designates, according to experience, a computing framework to execute the to-be-processed data task rather than selecting a computing framework according to operation time and resource consumption. Consequently, the data processing efficiency is relatively low, and working performance of the system is reduced.